Vegetation Stress Research Articles

Lichens are more tolerant against winter warming stress than vascular and non‐vascular plants: Insights from an alpine field experiment

Abstract Extreme weather events influence carbon cycling and lead to pervasive changes in ecosystem structure and function. Vegetation at high latitudes and in alpine bioclimatic zones can be particularly sensitive to winter warming events, which are short‐lived climatic events where temperatures are unusually high and often include rainfall. With climate change the frequency and severity of winter warming events are increasing. We report here from a field experiment on a lichen‐dominated ridge at a high mountain plateau in central Norway. This is a common vegetation type at high latitudes and altitudes, yet little is known about ecophysiological responses to winter warming events in lichens, and how it may differ from responses in bryophytes and vascular plants. We ran a week‐long simulation of vegetation stress from winter warming events through thaw–freeze and ice encasement, during late winter in 2021 and 2022. The thaw–freeze treatment had minor effects on summer ecophysiology in lichens (Cladonia mitis, Cetraria islandica and Nephromopsis nivalis), while the species N. nivalis and to a lesser extent, C. mitis had reduced vitality after the ice encasement treatment. Contrastingly, the bryophyte Polytrichum juniperinum, and vascular plant Empetrum nigrum had reduced photosynthetic efficiency and seasonal growth in both thaw–freeze and ice encasement treatments. The ice encasement treatment was overall more lethal and led to reduced NDVI (Normalised Difference Vegetation Index). However, reduction in vitality of vascular and non‐vascular plants was not enough to impact overall ecosystem carbon flux. Synthesis: The lichen's stronger tolerance against thaw–freeze and ice encasement than co‐existing plants oppose the general effects of summer climate warming, where lichens may succumb under greater plant‐growth and warmer soils. This study advocates for the importance of year‐round ecology to understand vegetation change under climate change.

Drought Assessment in Kalaburgi District, North-eastern Dry Zone of Karnataka, India by Using Remote Sensing and Google Earth Engine

Drought is a significant natural disaster exacerbated by global warming, leading to severe environmental, economic and societal impacts. As one of the most complex phenomena, drought requires advanced methods for effective monitoring and assessment. Remote sensing indices have proven effective in analyzing drought's geographical and temporal distribution. In this study, the semi-arid nature of the North-Eastern Dry Zone of Karnataka, characterized by low rainfall and high temperature, was examined for its vulnerability to drought. The Google Earth Engine (GEE) platform was utilized, which provides cloud-based access to advanced computational resources for processing multi-temporal satellite data. This approach minimizes the need for extensive data downloads and complex software operations, enabling efficient drought monitoring. In this study, GEE was applied to create and execute customized scripts for drought assessment, thereby accelerating the procedure and minimizing the need for extensive data downloads and complex software operations. The study focused on the North Eastern Dry Zone of Karnataka, particularly Kalaburgi district, employing the Normalized Difference Vegetation Index (NDVI) and Vegetation Condition Index (VCI) derived from MODIS data. The analysis revealed severe drought conditions, particularly in 2001, with NDVI values as low as 0.07 at Afzalpur station and 0.06 at Chitapur station, indicating significant vegetation stress. The VCI analysis further supported these findings, with values as low as 0.05 at Afzalpur station and 0.03 at Chitapur station, highlighting the drought's intensity. This integrated approach provides a reliable evaluation of agricultural drought, essential for enhancing drought management and mitigation strategies in the region.

Association of haloacid dehydrogenase and alcohol dehydrogenase with vegetative growth, virulence and stress tolerance during tea plant infection by Didymella segeticola

Tea leaf spot, caused by the fungus Didymella segeticola, occurs in the high-mountain tea plantations of Southwest China. Due to a limited understanding of the disease's epidemiology and the lack of comprehensive control measures, it has a significant negative impact on tea yield and quality. In this study, we revealed that D. segeticola infection begins when conidia germinate to form a germ tube on the leaf surface. The fungus then grows in the intercellular spaces of the leaf epidermal cells, invading tea tissue and causing necrotic lesions. This infection leads to significant alterations in the cell walls of spongy and palisade mesophyll cells, severely damaging chloroplasts. We employed transcriptomic and metabolomic analyses based on an in vitro infection model using matcha powder to uncover two key genes of D. segeticola: DsHAD (encoding holoacid dehydrogenase) and DsADH (encoding alcohol dehydrogenase). These genes are the first to be associated with conidiation, virulence, and sensitivity to oxidative stress. DsHAD regulates the virulence of D. segeticola by modulating glutamate homeostasis. Our results elucidate the infection strategy of D. segeticola on tea leaves and provide valuable data for future research on control measures for tea leaf spot.

AMELIORATION POTATO PLANT PERFORMANCE UNDER DROUGHT CONDITIONS IN IRAQ BY USING TITANIUM DIOXIDE, AND BIODEGRADING, BIODEGRADABLE TREATMENTS

This study was aimed to mitigate the issue of drought in potato production and quality using number of sustainable strategies. The experiment carried out at vegetable field of the College of Agricultural Engineering Sciences - University of Baghdad during spring season 2023. The experiment was conducted using split arrangement within Randomized Complete Block Design with two factors and three replicates (2X6X3). Applying TiO2-NPs represented the first factor (main plot) (10 mg.L-1), which symbolized (T0, T1). six treatments were included to represent subplots (regular irrigation interval (I) prolonged irrigation interval (D), fungal biofertilizers (DB), fungal biofertilizers + mannitol (DBM), fungal biofertilizers +xanthan (DBZ), fungal biofertilizers + mannitol+ xanthan (DBMZ). Results showed the superiority of spraying TiO2-NPs in all yield traits. Also the results revealed interaction treatment T1DBMX in producing significant results the entire vegetative growth traits and stress related enzymes concentrations in compare to control treatment T0D.

Fine-scale retrieval of leaf chlorophyll content using a semi-empirically accelerated 3D radiative transfer model

Leaf chlorophyll content (LCC) retrieval from remote sensing imagery is essential for monitoring vegetation growth and stress in the agroforestry industry. Many remote sensing inversion methods for estimating LCC primarily rely on 1D radiative transfer models (RTMs) that abstract canopies into horizontal layers or simple geometric primitives. Yet, this methodology faces challenges when applied to heterogeneous canopies, particularly in fine-scale mapping where each pixel's reflectance is significantly influenced by its surroundings, e.g. crown shadows. While 3D RTMs hold promise for addressing these challenges by explicitly describing complex canopy structures, their computational demands and the complexity involved in parameterizing detailed 3D structures limit the generation of extensive training datasets, requiring simulations across numerous parameter combinations. In this study, we used a semi-empirically accelerated 3D RTM, termed Semi-LESS, with a 1D residual network to accurately retrieve leaf chlorophyll content (LCC) from UAV images and LiDAR data at a 3-m resolution. We first reconstructed structures of forest plots using UAV LiDAR point cloud, based on which, UAV images with varying leaf and soil optical properties are simulated using the Semi-LESS. Subsequently, a training dataset consisting LCC and its corresponding reflectance was generated from the simulated UAV images by focusing on sunlit pixels. A 1D residual network is trained using the training dataset for LCC estimation. For comparison, we also trained an estimation model using a dataset generated from PROSAIL. The results show that estimation model trained with Semi-LESS surpasses PROSAIL in retrieving LCC from both simulation datasets and field measurements of two forest plots. The RMSE of Semi-LESS was 5.40–6.92 µg/cm2 for simulation datasets and 8.21–9.76 µg/cm2 for field measurements, whereas PROSAIL exhibited lower accuracy with an RMSE of 7.76–9.83 µg/cm2 for simulation datasets and 12.76–13.06 µg/cm2 in field measurements. The results demonstrate that Semi-LESS coupled with deep learning is reliable and has great potential for LCC mapping using UAV images, which is particularly useful for fine-scale applications such as crop and orchard monitoring. This approach also highlights the impact of shadows on LCC retrieval.

Comparing methods for solar-induced fluorescence efficiency estimation using radiative transfer modelling and airborne diurnal measurements of barley crops

Ability of remotely sensed solar-induced chlorophyll fluorescence (SIF) to serve as a vegetation productivity and stress indicator is impaired by confounding factors, such as varying crop-specific canopy structure, changing solar illumination angles, and SIF-soil optical interactions. This study investigates two normalisation approaches correcting diurnal top-of-canopy SIF observations retrieved from the O2-A absorption feature at 760 nm (F760 hereafter) of summer barley crops for these confounding effects. Nadir SIF data was acquired over nine breeding experimental plots simultaneously by an airborne imaging spectrometer (HyPlant) and a drone-based high-performance point spectrometer (AirSIF). Ancillary measurements, including leaf pigment contents retrieved from drone hyperspectral imagery, destructively sampled leaf area index (LAI), and leaf water and dry matter contents, were used to test the two normalisation methods that are based on: i) the fluorescence correction vegetation index (FCVI), and ii) three versions of the near-infrared reflectance of vegetation (NIRV). Modelling in the discrete anisotropic radiative transfer (DART) model revealed close matches for NIRv-based approaches when corrected canopy SIF was compared to simulated total chlorophyll fluorescence emitted by leaves (R2 = 0.99). Normalisation with the FCVI also performed acceptably (R2 = 0.93), however, it was sensitive to variations in LAI when compared to leaf emitted chlorophyll fluorescence efficiency. Based on the results modelled in DART, the NIRvH1 normalisation was found to have a superior performance over the other NIRv variations and the FCVI normalisation. Comparison of the SIF escape fractions suggests that the escape fraction estimated with NIRvH1 matched escape fraction extracted from DART more closely. When applied to the experimental drone and airborne nadir canopy SIF data, the agreement between NIRvH1 and FCVI produced chlorophyll fluorescence efficiency was very high (R2 = 0.93). Nevertheless, NIRvH1 showed higher uncertainties for areas with low vegetation cover indicating an unaccounted contribution of SIF-soil interactions. The diurnal courses of chlorophyll fluorescence efficiency for both approaches differed not significantly from simple normalisation by incoming and apparent photosynthetically active radiation. In conclusion, SIF normalisation with NIRvH1 more accurately compensates the effects of canopy structure on top of canopy far red SIF, but when applied to top of canopy in-situ data of spring barley, the effects of NIRvH1 and FCVI on the diurnal course of SIF had a similar influence.

Early warning of drought-induced vegetation stress using multiple satellite-based ecological indicators

Droughts have posed, and continue to pose, severe risks to terrestrial ecosystems. Particularly against the backdrop of global climate change, the intensity and frequency of extreme droughts are expected to further aggravate. However, a significant gap persists in early drought warning for vegetation monitoring. Therefore, this study examined the spatial and temporal dynamics of two summer drought events happened in Southwest China in 2011 and 2022, and analyzed the early responses of four ecological indicators including global Orbiting Carbon Observatory-2 (OCO-2) SIF dataset (GOSIF), the leaf-scale fluorescence yield (Φf), the near-infrared reflectance of vegetation (NIRv) andthe normalized difference vegetation index (NDVI) to drought extremes. All these indicators successfully captured the drought-induced vegetation stress, but as a proxy for vegetation photosynthesis, GOSIF was the most sensitive. Specifically, during the 2022 drought, GOSIF fell below the baseline year as early as day of year (DOY) 193, whereas NIRv and NDVI began at DOY 201, and Φf lagged severely. Similar behaviour was also found in the drought period of 2011. Overall, compared to the baseline year, GOSIF, Φf, NIRv and NDVI decreased by 96.93 %, 54.11 %, 43.92 % and 17.03 % in 2011, and reduced by 70.00 %, 42.01 %, 48.74 % and 19.53 % in 2022, respectively. During the past two decades, GOSIF exhibited the strongest correlation with drought intensity (r = 0.880, p < 0.05), followed by NIRv (r = 0.875, p < 0.05) and NDVI (r = 0.871, p < 0.05), and Φf was the weakest (r = 0.432, p > 0.05). Spatially, the proportion of areas where the correlations exceeded 0.6 by GOSIF and NIRv were 42.39 % and 39.32 %, respectively. In summary, this study demonstrated that global re-constructed GOSIF possesses considerable potential as an early warning indicator for vegetation drought.

Geospatial Analysis of Climate Change Impacts on Vegetation Dynamics in Owerri West, Imo State

Climate change is a global phenomenon with profound effects on the environment, economies, and societies. It refers to significant changes in global temperatures and weather patterns over time. This study explores the impact of climate change on vegetation in Owerri West, Imo State, Nigeria, using geospatial techniques over a 20-year period (2003–2023). By analyzing satellite imagery and Normalized Difference Vegetation Index (NDVI) values alongside key climatic variables such as precipitation and land surface temperature (LST), the research seeks to quantify the extent of vegetation changes due to climate variability. The study uses Landsat 8 satellite data to evaluate spatiotemporal trends in vegetation health. NDVI values in 2003 ranged from a high of 0.45, indicating healthy and dense vegetation, to a low of -0.04, representing barren areas. By 2013, NDVI values had drastically declined, with a maximum of 0.12 and a minimum of -0.33, despite an increase in precipitation from 3300 mm (2003) to 3900 mm (2013). This decline suggests that other factors, such as extreme weather events or urbanization, contributed to vegetation stress. In 2023, NDVI values showed partial recovery, with a maximum of 0.28 and a minimum of 0.05, although precipitation levels dropped to a range of 600 mm to 540 mm. The broader temperature range in 2023, with a maximum of 32°C and a minimum of 17°C, likely reduced heat stress, allowing for vegetation recovery, though still below the 2003 levels, The findings highlight the complex interplay between climatic variables and vegetation health, where precipitation and temperature changes significantly influence vegetation dynamics. However, the NDVI decline in 2013, despite high precipitation, suggests that anthropogenic factors like urbanization and land use changes also play a critical role. This research emphasizes the importance of integrating remote sensing and GIS for monitoring vegetation responses to climate change. The study calls for sustainable land management practices and climate adaptation strategies to enhance vegetation resilience in the region.

Google Earth Engine application for mapping and monitoring drought patterns and trends: A case study in Arkansas, USA

Drought is a prolonged dry period that can have severe impacts on the environment, human health, economies, agriculture, and energy resources. It can lead to water shortages, ruin crops, dry out forests, and reduce the availability of food and water for wildlife and livestock. The primary objectives of this study are to: 1) quantify the variability and distributions of drought patterns in Arkansas, United States (US), 2) use remotely sensed indices to investigate the correlation between drought and vegetation cover in the area, and 3) develop a cloud-based framework (user-friendly app) to facilitate the assessment of drought impact in Arkansas over the past decades. A correlation analysis was also performed between the Vegetation Health Index (VHI) and meteorological indices to better understand the impact of meteorological drought on vegetation stress. In addition, Mann-Kendall trend analysis was used to assess trends in meteorological drought indices. The results indicate that drought is most prevalent during March and August months. The results of this study revealed that approximately 31% of the study area fell under the four drought classes (i.e., 1% Extreme drought, 4% Severe drought, 9% moderate drought, and 19% mild drought), with spring and the growing season experiencing moderate drought, particularly in agricultural areas, most notably within the Mississippi Alluvial Valley Plain at both state and county levels. In August, approximately 31% of the study area fell under the Four drought classes (i.e., 1% Extreme drought, 4% Severe drought, 9% moderate drought, and 19% mild drought), with spring and the growing season experiencing moderate drought, particularly in agricultural areas, most notably within the Mississippi Alluvial Valley Plain at both state and county levels. This study provides an essential foundation for policymakers, environmental scientists, and agricultural stakeholders aiming to mitigate drought impacts and safeguard against future climate uncertainties.

Drought dynamics in California and Mississippi: A wavelet analysis of meteorological to agricultural drought transition

Understanding the transition from meteorological to agricultural drought is crucial for developing effective drought management strategies and early warning systems. This study provides a unique perspective by utilizing hybrid drought indices to explore the temporal and spatial complexities of drought propagation across two large watersheds—California and Mississippi—that feature distinct agro-climatic conditions and irrigation practices. We assess the links between meteorological drought, measured by the Standardized Precipitation Index (SPI), and agricultural drought using three indicators: Vegetation Drought Response Index (VegDRI), GRACE Root Zone Soil Moisture Percentile (SMI), and the Evaporative Demand Drought Index (EDDI). By comparing watersheds with different irrigation systems, we also consider the role of irrigation in modifying drought dynamics. Our analysis identified the 14-day SPI as particularly effective in capturing drought dynamics. Soil moisture showed the earliest response to rainfall anomalies, with a lag of 41 days in California and 59 days in Mississippi. Vegetation stress followed, with a lag of 137 days in California and 75 days in Mississippi, while atmospheric conditions lagged by 143 and 139 days, respectively. Irrigation was found to delay drought impacts by up to two months. This framework offers a finer temporal resolution and a more nuanced understanding of drought propagation, potentially informing more targeted drought mitigation strategies than traditional methods.

Detection of underground natural gas pipeline micro-leakage based on UAV hyperspectral remote sensing and GIS

ABSTRACT Detection of underground natural gas pipeline micro-leakage based on unmanned aerial vehicle (UAV) hyperspectral remote sensing and GIS. Hyperspectral images can indirectly detect underground natural gas pipeline micro-leakage through spectral and spatial variation characteristics of surface vegetation. However, most of existing studies were based on ground-mounted platforms, which could only perform small-range single-point detection and might occur misidentifications. UAV hyperspectral remote sensing can allow wide-range detection of surface vegetation. Moreover, underground pipeline distribution GIS data can provide prior knowledge about leakage points to exclude misidentifications. Therefore, a natural gas pipeline micro-leakage experiment was set up. UAV hyperspectral images of grasslands and pipeline distribution vector data were obtained, which led to a proposed new wide-range multi-points detection methodology of underground natural gas micro-leakage. Firstly, the vegetation identification index (WVI) R 470 + R 674 / R 555 + R 750 was designed based on WOA–VMD (whale optimization algorithm – variational modal decomposition) to segment and extract the vegetation stress zones. Then, UAV- and GIS-based natural gas micro-leakage vegetation stress identification model was constructed to obtain the leak points of natural gas micro-leakage. Finally, the recognition ability was evaluated by comparing with three stress vegetation identification indices proposed in previous studies. The result showed that there was no missing or false detection in identification results; the identification and positioning effect was better than other indices, which could meet the practical application requirements.

Characterization and expression analysis of the MADS-box gene AGL8 in cotton: insights into gene function differentiation in plant growth and stress resistance.

AGAMOUS-LIKE 8 (AGL8) belongs to the MADS-box family, which plays important roles in transcriptional regulation, sequence-specific DNA binding and other biological processes and molecular functions. The genome of cotton, a representative polyploid plant, contains multiple AGL8 genes. However, their functional differentiation is still unclear. In this study, a comprehensive genomic analysis of AGL8 genes was conducted. Cotton AGL8s were subdivided into four subgroups (Groups 1, 2, 3, and 4) based on phylogenetic analysis, and different subgroups of AGL8s presented different characteristics, including different structures and conserved motifs. With respect to the promoter regions of the GhAGL8 genes, we successfully predicted cis-elements that respond to phytohormone signal transduction and the stress response of plants. Transcriptome data and real-time quantitative PCR validation indicated that three genes, namely, GH_D07G0744, GH_A03G0856 and GH_A07G0749, were highly induced by methyl jasmonate (MeJA), salicylic acid (SA), and abscisic acid (ABA), which indicated that they function in plant resistance to abiotic and biotic stresses. The information from the gene structure, number and types of conserved domains, tissue-specific expression levels, and expression patterns under different treatments highlights the differences in sequence and function of the cotton AGL8 genes. Different AGL8s play roles in vegetative growth, reproductive development, and plant stress resistance. These results lay a foundation for further study of GhAGL8s in cotton.

Disentangling the Effects of Atmospheric and Soil Dryness on Autumn Phenology across the Northern Hemisphere

In recent decades, drought has intensified along with continuous global warming, significantly impacting terrestrial vegetation. High atmospheric water demand, indicated by vapor pressure deficit (VPD), and insufficient soil moisture (SM) are considered the primary factors causing drought stress in vegetation. However, the influences of VPD and SM on the autumn phenology are still unknown. Using satellite observations and meteorological data, we examined the impacts of VPD and SM on the end of the growing season (EOS) across the Northern Hemisphere (&gt;30°N) from 1982 to 2022. We found that VPD and SM were as important as temperature, precipitation, and radiation in controlling the variations in the EOS. Moreover, the EOS was predominantly influenced by VPD or SM in more than one-third (33.8%) of the study area. In particular, a ridge regression analysis indicated that the EOS was more sensitive to VPD than to SM and the other climatic factors, with 25% of the pixels showing the highest sensitivity to VPD. In addition, the effects of VPD and SM on the EOS varied among biome types and climate zones. VPD significantly advanced the EOS in 25.8% of temperate grasslands, while SM had the greatest impact on advancing the EOS in 17.7% of temperate coniferous forests. Additionally, 27.7% of the midlatitude steppe (BSk) exhibited a significant negative correlation between VPD and the EOS, while 19.4% of the marine west coast climate (Cfb) showed a positive correlation between SM and the EOS. We also demonstrated that the correlation between VPD and the EOS was linearly affected by VPD and the leaf area index, while the correlation between SM and the EOS was affected by SM, precipitation, and the leaf area index. Our study highlights the importance of VPD and SM in regulating autumn phenology and enhances our understanding of terrestrial ecosystem responses to climate change.

Quantitative assessment of Hurricane Ian’s damage on urban vegetation dynamics utilizing Landsat 9 in Fort Myers, Florida

Florida’s unique climatic and geographical features have profoundly influenced its hurricane history. This study quantitatively examines the effects of Hurricane Ian on urban vegetation in Fort Myers, Florida, using remote sensing data. We analyzed pre- and post-hurricane vegetation indices, including NDVI (Normalized Difference Vegetation Index), ARVI (Atmospherically Resistant Vegetation Index), and SAVI (Soil-Adjusted Vegetation Index). Our findings reveal varied spatial impacts, with NDVI changes ranging from −0.03 to 0.333, ARVI changes from −0.016 to 0.25, and SAVI changes from −0.04 to 0.5. Negative values indicate vegetation damage, while positive values suggest resilience or recovery. The study area experienced a 63.75% reduction in vegetation cover, from 67.10 km2 before Hurricane Ian to 24.325 km2 after. Pre-hurricane NDVI ranged from −0.2298 to 0.5663, while post-hurricane values ranged from −0.189 to 0.521, indicating overall vegetation stress. ARVI maxima decreased from 0.379 to 0.352, and SAVI maxima from 0.849 to 0.782, further confirming vegetation damage. Support Vector Machine classification achieved 89% accuracy (Kappa = 0.85) for pre-hurricane and 87% (Kappa = 0.83) for post-hurricane vegetation mapping. These findings enhance our understanding of hurricane impacts on urban green infrastructure, with significant implications for urban planning and disaster preparedness in coastal cities prone to extreme weather events. The outcomes enhance damage assessment methodologies and provide valuable insights into the ecological consequences of hurricanes on urban ecosystems.

Improved Methods for Retrieval of Chlorophyll Fluorescence from Satellite Observation in the Far-Red Band Using Singular Value Decomposition Algorithm

Solar-induced chlorophyll fluorescence (SIF) is highly correlated with photosynthesis and can be used for estimating gross primary productivity (GPP) and monitoring vegetation stress. The far-red band of the solar Fraunhofer lines (FLs) is close to the strongest SIF emission peak and is unaffected by chlorophyll absorption, making it suitable for SIF intensity retrieval. In this study, we propose a retrieval window for far-red SIF, significantly enhancing the sensitivity of data-driven methods to SIF signals near 757 nm. This window introduces a weak O2 absorption band based on the FLs window, allowing for better separation of SIF signals from satellite spectra by altering the shape of specific singular vectors. Additionally, a frequency shift correction algorithm based on standard non-shifted reference spectra is proposed to discuss and eliminate the influence of the Doppler effect. SIF intensity retrieval was achieved using data from the GOSAT satellite, and the retrieved SIF was validated using GPP, enhanced vegetation index (EVI) from the MODIS platform, and published GOSAT SIF products. The validation results indicate that the SIF products provided in this study exhibit higher fitting goodness with GPP and EVI at high spatiotemporal resolutions, with improvements ranging from 55% to 129%. At low spatiotemporal resolutions, the SIF product provided in this study shows higher consistency with EVI and GPP spatially.

Exploring Relationships Between Drought Indices and Ecological Drought Impacts Using Machine Learning and Explainable AI

Rangeland ecosystems in the United States hold great ecological, economic, and cultural value. However, the increasing frequency and severity of droughts pose potential threats to these ecosystems. This study used remotely sensed data, machine learning, and explainable artificial intelligence (XAI) to explore relationships between drought indices and vegetation health in the Cheyenne River Basin, USA. The study employed XGBoost Regressor and Extra Trees Regressor models in conjunction with SHapley Additive exPlanations (SHAP) to identify the most influential drought indices and environmental variables for predicting Normalized Difference Vegetation Index (NDVI), thereby uncovering indicators of vegetation stress in the basin. The XGBoost regressor was moderately successful at predicting NDVI, making the model suitable for subsequent XAI analysis using SHAP. SHAP results revealed that the Palmer Drought Severity Index (PDSI), the 90-day Standardized Precipitation Index (SPI), and snow water equivalent (SWE) were the most influential predictors of NDVI, indicating their strong association with changes in vegetation health in the Cheyenne River Basin. This study demonstrates the feasibility and value of applying XAI methods to investigate both the strength and directionality of ecological drought indicators—an approach that has been underutilized in drought research. These insights can inform future drought research, improve monitoring efforts, and help anticipate ecological drought impacts in the region.

Rapid impact assessment of severe cyclone storm Michaung along coastal zones of Andhra and Tamil Nadu, India: A geospatial analysis

The coastal regions of India, particularly the Bay of Bengal, are highly vulnerable to the severe weather conditions induced by tropical cyclones. This study presents a comprehensive analysis of the changes in vegetation cover, shoreline dynamics, and meteorological variations resulting from Cyclone Michaung and subsequent post-monsoon events along the coastal zones of Andhra Pradesh and Tamil Nadu, India. A suite of vegetation indices, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Modified Vegetation Condition Index (mVCI), and Disaster Vegetation Damage Index (DVDI), were employed to assess changes in vegetation cover. The Digital Shoreline Assessment System (DSAS) was utilized to evaluate shoreline changes, and a range of meteorological variables were analyzed to assess the impacts of Cyclone Michaung and post-monsoon events. The findings reveal significant ecological impacts, with a notable decrease in Very Healthy Vegetation from 5.71% to 1.30%. The mean value of mVCI shifted from −0.2 to −0.16, indicating vegetation stress. DVDI analysis showed that 56.49% of the area experienced moderate damage, while 40.24% suffered severe vegetation damage. Additionally, erosion was observed along 79.46% of the shoreline transects in the study area. These insights are critical for assisting coastal managers in developing resilient coastal systems. Remarkably, a significant change in rainfall was recorded between the pre-cyclone period and the landfall day, with maximum rainfall intensifying from 13.93 mm/h on December 3rd to 164.26 mm/h on December 4th, and subsequently decreasing to 144.39 mm/h on December 5th.

Hyperspectral Estimation of Chlorophyll Content in Wheat under CO2 Stress Based on Fractional Order Differentiation and Continuous Wavelet Transforms

The leaf chlorophyll content (LCC) of winter wheat, an important food crop widely grown worldwide, is a key indicator for assessing its growth and health status in response to CO2 stress. However, the remote sensing quantitative estimation of winter wheat LCC under CO2 stress conditions also faces challenges such as an unclear spectral sensitivity range, baseline drift, overlapping spectral peaks, and complex spectral response due to CO2 stress changes. To address these challenges, this study introduced the fractional order derivative (FOD) and continuous wavelet transform (CWT) techniques into the estimation of winter wheat LCC. Combined with the raw hyperspectral data, we deeply analyzed the spectral response characteristics of winter wheat LCC under CO2 stress. We proposed a stacking model including multiple linear regression (MLR), decision tree regression (DTR), random forest (RF), and adaptive boosting (AdaBoost) to filter the optimal combination from a large number of feature variables. We use a dual-band combination and vegetation index strategy to achieve the accurate estimation of LCC in winter wheat under CO2 stress. The results showed that (1) the FOD and CWT methods significantly improved the correlation between the raw spectral reflectance and LCC of winter wheat under CO2 stress. (2) The 1.2-order derivative dual-band index (RVI (R720, R522)) constructed by combining the sensitive spectral bands of the CO2 response of winter wheat leaves achieved a high-precision estimation of the LCC under CO2 stress conditions (R2 = 0.901). Meanwhile, the red-edged vegetation stress index (RVSI) constructed based on the CWT technique at specific scales also demonstrated good performance in LCC estimation (R2 = 0.880), verifying the effectiveness of the multi-scale analysis in revealing the mechanism of the CO2 impact on winter wheat. (3) By stacking the sensitive spectral features extracted by combining the FOD and CWT methods, we further improved the LCC estimation accuracy (R2 = 0.906). This study not only provides a scientific basis and technical support for the accurate estimation of LCC in winter wheat under CO2 stress but also provides new ideas and methods for coping with climate change, optimizing crop-growing conditions, and improving crop yield and quality in agricultural management. The proposed method is also of great reference value for estimating physiological parameters of other crops under similar environmental stresses.

Are the ecosystem-level evaporative stress indices representative of evaporative stress of vegetation?

Evaporative Stress Index (ESI), also sometimes referred as Evaporative Stress Ratio (ESR), has been widely used as an indicator of vegetation evaporative stress, and is often used to track forest and agriculture droughts. Lower the stress, higher is the value of ESI or ESR. The goal of this study is to assess the suitability of these indices for tracking vegetation evaporative stress. As the dynamics of water loss from vegetation through transpiration (T) can be different than that of evapotranspiration (ET) from the ecosystem, it is hypothesized that ESI or ESR may not be sufficiently representative of the vegetation evaporative stress. Using eddy covariance flux tower data of 518 site years, distributed across 49-sites and 9 land covers globally, our findings reveal underestimation of vegetation evaporative stress by ESI during periods of high vapor pressure deficit (VPD) and overestimation during dry, low-VPD periods. The results highlight the need to improve representativeness of ESI for monitoring vegetation evaporative stress. Notably, this may entail accurate estimation of ecosystem T in systems lacking in-situ data, a challenge that warrants further attention.

Global identification of LIM genes in response to different heat stress regimes in Lactuca sativa

BackgroundLIM (Lineage-11 (LIN-11), Insulin-1 (ISL-1), and Mechanotransduction-3 (MEC-3)) genes belong to a family that hold ubiquitous properties contributing to organ, seed, and pollen development as well as developmental and cellular responses to biotic and abiotic stresses. Lettuce (Lactuca sativa) is a highly consumed vegetable crop susceptible heat stress. High temperatures limit lettuce’s overall yield, quality and marketability. Lettuce LIM genes have not been identified and their role in response to high temperatures is not known. Aiming to identify potential new targets for thermoresilience, we searched for LIM genes in lettuce and compared them with orthologous of several dicotyledons and monocotyledons plant species.ResultsWe identified fourteen lettuce LIM genes distributed into eight different subgroups using a genome-wide analysis strategy. Three belonging to DAR (DA means “large” in Chinese) class I, two DAR class II, one in the WLIM1, two in the WLIM2, one in the PLIM1, two in the PLIM2 class, one ßLIM and two δLIMs. No DAR-like were identified in any of the species analyzed including lettuce. Interestingly, unlike other gene families in lettuce which underwent large genome tandem duplications, LIM genes did not increase in number compared to other plant species. The response to heat stress induced a dynamic transcriptional response on LsLIM genes. All heat stress regimes, including night stress, day stress and day and night stress were largely responsible for changes in LIM transcriptional expression.ConclusionsOur global analysis at the genome level provides a detailed identification of LIM genes in lettuce and other dicotyledonous and monocotyledonous plant species. Gene structure, physical and chemical properties as well as chromosomal location and Cis-regulatory element analysis together with our gene expression analysis under different temperature regimes identified LsWLIM1, LsWLIM2b, LsDAR3 and LsDAR5 as candidate genes that could be used by breeding programs aiming to produce lettuce varieties able to withstand high temperatures.